Control method

ABSTRACT

The object of the present disclosure is to present a layout reflecting a preference. The control method includes: outputting an estimated feature value based on first layout data generated by a user; generating an album creation parameter based on the output estimated feature value; generating layout data based on the generated album creation parameter and an image used for second layout data generated by a user; updating the album creation parameter based on a feature value based on the generated layout data and a feature value based on the second layout data; and performing verification of the updated album creation parameter.

BACKGROUND OF THE DISCLOSURE Field of the Disclosure

The present disclosure relates to a technique to create a photo album.

Description of the Related Art

Japanese Patent Laid-Open No. 2017-038343 discloses software forcreating a photo album by selecting photos that are used in the albumfrom among a plurality of photos and automatically laying out theselected photos. The function such as this to automatically selectphotos and layout the photos in an album is called the automatic layoutfunction. The automatic layout function analyzes a plurality of photos,gives a score to each photo by analyzing the photo, and preferentiallyadopts a photo whose score is high for the layout of an album.

SUMMARY OF THE DISCLOSURE

The degree of satisfaction for a photo album created by the automaticlayout is related to not only the image quality but also the personalpreference of a user. Consequently, an object of the present disclosureis to make it possible to present a layout more reflecting thepreference of a user.

One embodiment of the present disclosure is a control method including:outputting an estimated feature value based on first layout datagenerated by a user; generating an album creation parameter based on theoutput estimated feature value; generating layout data based on thegenerated album creation parameter and an image used for second layoutdata generated by a user; updating the album creation parameter based ona feature value based on the generated layout data and a feature valuebased on the second layout data; and performing verification of theupdated album creation parameter.

Further features of the present disclosure will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A to FIG. 1C are diagrams showing a system configuration of analbum creation service;

FIG. 2 is a sequence diagram relating to album creation;

FIG. 3 is a diagram showing a settlement UI screen;

FIG. 4 is a sequence diagram relating to learning of automatic layoutcontrol;

FIG. 5 is a flowchart of processing of album data creation and albumordering at a user terminal;

FIG. 6 is a diagram showing a setting UI screen;

FIG. 7 is a diagram showing an image selection UI screen;

FIG. 8 is a diagram showing a layout editing UI screen;

FIG. 9 is a flowchart of automatic layout processing;

FIG. 10 is a flowchart of learning processing performed on a cloudserver;

FIG. 11 is a flowchart of allocation processing of GroupID;

FIG. 12 is an explanatory diagram of learning;

FIG. 13 is a detailed functional block diagram of a target album featurevalue estimation unit;

FIG. 14 is a flowchart of learning processing;

FIG. 15A and FIG. 15B are each an explanatory diagram of a settingmethod of learning data and verification data;

FIG. 16A to FIG. 16C are each a diagram showing a data structure of analbum creation parameter; and

FIG. 17 is a diagram showing a verification example of an album creationparameter.

DESCRIPTION OF THE EMBODIMENTS First Embodiment

<About Configuration of System>

In the following, the configuration of an album creation system in thepresent embodiment is explained by using FIG. 1A to FIG. 1C. FIG. 1A isa diagram showing the entire configuration of the album creation systemin the present embodiment. As shown in FIG. 1A, this system has a userterminal 100 and a cloud server 110 connected to the user terminal 100and it is possible to perform transmission or reception of data betweenthe user terminal 100 and the cloud server 110. As the user terminal100, a tablet, a smartphone, an information processing apparatus(hereinafter, PC), or the like is supposed.

FIG. 1B is a block diagram showing the hardware configuration of theuser terminal 100 functioning as an image processing apparatus. A CPU101 centralizedly controls the operation of the user terminal 100 byloading control programs stored in a ROM 103 onto a RAM 104 andperforming various kinds of control, such as image processing control,by reading the control programs as needed.

A GPU 102 controls the display of a display 106 by performingcalculation processing that is necessary at the time of performing imagedrawing. In recent years, there is a case where the GPU is used as anaccelerator of artificial intelligence processing that requires ahigh-load parallel calculation.

In the ROM 103, various programs, such as an activation program of theuser terminal 100, are stored. In the present embodiment, as the ROM103, a flash storage or the like is supposed. The RAM 104 is a mainstorage device of the CPU 101 and used as a work area or a temporarystorage area for loading various programs stored in the ROM 103.

A hard disk drive (hereinafter, HDD) 105 is a large-capacity storagedevice. In the HDD 105, application programs, such as an album creationapplication, image data, and the like are saved.

On the display 106, application processing results (for example,double-page spread of an album and the like) are displayed.

A neural processing unit (hereinafter, NPU) 107 is an artificialintelligence dedicated chip incorporating a neural network thatsimulates the human cerebral nerve system. By incorporating the NPU 107in the user terminal 100, it is made possible for the terminal alone toperform image recognition and natural language processing using themachine learning (for example, deep learning), which are conventionallyperformed on the basis of a cloud.

To an input I/F 108, an input device, such as a keyboard and a mouse, isconnected (not shown schematically). The input I/F 108 receives userinstructions from the input device such as this and transmits input datato each module of an application implemented by the CPU 101.

A communication I/F 109 has a USB I/F and a network I/F and the userterminal 100 is connected with the external cloud server 110 via thecommunication I/F 109.

FIG. 1C a block diagram showing the hardware configuration of the cloudserver 110. The basic configuration of the cloud server 110 thatfunctions as an image processing apparatus is the same as that of theuser terminal 100. However, in general, the performance of the CPU, theGPU, and the communication I/F of the cloud server is higher than thatof the CPU, the GPU, and the communication I/F of the user terminal andthe capacity of the HDD, the ROM, and the RAM of the cloud server islarger than that of the HDD, the ROM, and the RAM of the user terminal.Here, the configuration is described in which the cloud server 110 hasneither display nor NPU, but it may also be possible for the cloudserver 110 to have a display and an NPU.

<About Album Creation>

In the following, album creation in the present embodiment is explainedby using FIG. 2. FIG. 2 is a sequence diagram showing a general flow ofalbum creation in the present embodiment and shows an example of analbum creation service that is provided by the user terminal 100, thecloud server 110, and a print laboratory 200.

A user boots an album creation application (hereinafter, simplydescribed as application) on the user terminal 100 and givesinstructions to create an album in a predetermined format by usingcaptured and collected photos via a UI of the application. In FIG. 1Aand FIG. 2, an aspect is shown in which the album creation system hasone user terminal, but the number of user terminals connected to thealbum creation system may be two or more and in this case, the cloudserver is connected with the user terminals of a plurality of users.

The cloud server 110 is connected with the user terminal 100 and a PC ofthe print laboratory 200 via a network. The installer of the applicationis stored in the cloud server 110 and a user downloads the installer asneeded. In a case where album data created via the application isuploaded to the cloud server 110, the cloud server 110 temporarily savesthe album data. Further, the user performs so-called billing processing,such as fee payment processing, via a settlement UI screen (see FIG. 3)on the application. The cloud server 110 transfers the album data forwhich the proceedings to request for album creation, such as billingprocessing, have been completed to the PC of the print laboratory 200.

The print laboratory 200 comprises a PC (see FIG. 1B) whoseconfiguration is the same as that of the user terminal 100 and aprinting apparatus. The PC of the print laboratory 200 receives thealbum data for which the proceedings to request for album creationincluding billing processing have been completed from the cloud server110. In the print laboratory 200, album creation based on the albumdata, specifically, printing and bookbinding are performed. A createdalbum is sent to a user. The PC of the print laboratory 200 notifies theuser terminal 100 of sending of the album.

The procedure of the actual album creation is as follows. In a casewhere the application is not installed in the user terminal 100 at thetime of a user desiring to create an album, the user makes an attempt todownload the application from a WEB site or the like of a company thatprovides the album creation service by operating the user terminal 100.At this time, as indicated by symbol 201, the user terminal 100transmits a download request for the application to the cloud server110.

The cloud server 110 having received the download request transmitted bythe user terminal 100 transmits the installer of the application to theuser terminal 100 as indicated by symbol 202. In the user terminal 100having received the installer transmitted by the cloud server 110, theinstallation of the application is performed as indicated by symbol 203.By the installation of the application, it is made possible for s userto create an album by using the user terminal 100.

As indicated by symbol 204, a user activates the application installedin the user terminal 100 and creates an album on the application. Theapplication has a function (automatic layout function) to automaticallycreate an album by selecting photo images and arranging the selectedphoto images on a double-page spread. It may also be possible for a userto automatically or manually edit the album created by making use of theautomatic layout function on the application. Further, the applicationalso has a function to upload the album data created via this to thecloud server 110.

As indicated by symbol 205, the user terminal 100 uploads the album datacreated by using the application to the cloud server 110. Further,although not shown in FIG. 2, the user terminal 100 also performsprocessing relating to the ordering of an album, such as billingprocessing, as well as uploading of the album data to the cloud server110.

The album data that is uploaded to the cloud server 110 may not be thealbum data itself (double-page spread data in which images are arrangedin templates) generated in the user terminal 100. For example, thetemplate data used in the album data may not be included. That is, thealbum data may data not including the template data but includingidentification information identifying templates, arranged image data,and information relating to the arrangement position. In this case, inthe cloud server 110 or the print laboratory 200, the layout isperformed again based on the identification information identifying thetemplate data and the information relating to the arrangement position,and thereby, the album data is generated.

In a case of receiving the completion notification of the album orderingprocessing, the cloud server 110 determines that an album has beenordered and as indicated by symbol 206, transfers the album datareceived from the user terminal 100 to the PC of the print laboratory200.

As indicated by symbol 207, in the print laboratory 200, album creationbased on the album data transferred to the PC (specifically, printingusing a printing machine, bookbinding using a bookbinding machine) isperformed.

A material type of the album (bookbinding-completed product) created inthe print laboratory 200 is sent to a user from the print laboratory200. Further, as indicated by symbol 208, the PC of the print laboratory200 transmits a sending notification of the material type of the albumto the cloud server 110. This sending notification is also transmittedto the cloud server 110 and in the cloud server 110 having received thesending notification, the album order management based on the contentsof the sending notification is performed.

In FIG. 2, the case is shown where the application is not installed inthe user terminal 100 and the installer of the application isdownloaded, but the present embodiment is not limited to this case. Forexample, it is also possible to apply the present embodiment to a casewhere the application is already installed in the user terminal 100(but, not the latest version) and the application of the latest versionis downloaded.

<About Learning of Automatic Layout Control>

In the following, the learning of the automatic layout control in thepresent embodiment is explained by using FIG. 4. FIG. 4 shows a casewhere the learning of the automatic layout control is performed in thecloud server 110, but the learning of the automatic layout control maybe performed in the PC of the print laboratory 200, Further, thelearning of the automatic layout control may be performed on the userterminal 100.

In a case of receiving album ordering instructions from a user, asindicated by symbol 401, the cloud server 110 starts the learning forthe automatic layout control. The learning for the automatic layoutcontrol refers to analyzing the preference and tendency of a user byusing album data indicating the contents of the album ordered by a user,and the like. More specifically, the learning for the automatic layoutcontrol refers to deriving each value of the parameter (referred to asalbum creation parameter) necessary at the time of automatic layoutprocessing. The learning for the automatic layout control and the albumcreation parameter will be described later.

As indicated by symbol 402, in accordance with learning results of theautomatic layout control, the cloud server 110 updates the albumcreation parameter by the value derived by the learning describedpreviously.

As indicated by symbol 403, at the time of updating of the applicationexplained in FIG. 2, the updating results of the album creationparameter are reflected. Due to this, at the time of a user activatingthe application next time on the user terminal 100, each value of theupdated album creation parameter is loaded. Consequently, it is madepossible to perform the automatic layout processing based on the updatedalbum creation parameter in the user terminal 100.

<About Album Creation Processing in User Terminal>

In the following, the album creation processing (204 in FIG. 2)performed in the user terminal 100 in the present embodiment isexplained by using FIG. 5.

At step S501, the updating of the application is performed. In a casewhere the application is activated in the user terminal 100, the userterminal 100 accesses the predetermined cloud server 110. At this time,in a case where the latest version of the application that is notinstalled yet in the user terminal 100 exists, the user terminal 100transmits a download request for the latest version to the cloud server110 and receives the latest version as a response to this request. Afterthat, in the user terminal 100, the updating of the application by theinstallation of the received latest version is performed. At the time ofthe updating of the application, the user terminal 100 also acquires thelatest version of the parameter necessary for album creation. In thefollowing, “step S-” is simply described as “S-”.

In a case where the application is activated and the application and thealbum creation parameter are updated at S501, the processing advances toS502. At S502, a setting UI screen as illustrated in FIG. 6 isdisplayed. A user selectively determines specifications of the albumthat is ordered, such as the size of the album, the type of paper, thenumber of pages, and the bookbinding method, via the setting UI screen.In a case where the user determines the specifications of the album andpresses down a “Next” button 601, the processing advances to imageselection at S503.

At S503, as a UI of the application, a screen for selecting photo imagesto be used in the album (referred to as image selection UI screen) isdisplayed. A user selects images to be used in the album via the imageselection UI screen. In a case where images are selected by the user,the processing advances to S504.

Here, the image selection UI screen is explained by using FIG. 7. FIG. 7shows an example of the image selection UI screen. An image selectionwindow 700 has a folder display area 701 and a selection area 702.

The folder display area 701 is an area for displaying the folderstructure within the HDD 105. In the folder display area 701, imagefolders are displayed in a hierarchical manner. In a case where theimage selection UI screen is displayed, first, a user selects a desiredfolder. The example in FIG. 7 shows the state where a folder whose nameis “02-01” is selected as a folder desired by the user and in thisfolder, images of photos captured Feb. 1, 2019 are saved.

The selection area 702 is an area for a user to select photos to be usedin the album and in which a thumbnail image of each photo saved in thespecified folder is displayed. In the example in FIG. 7, to thethumbnail image of the photo selected by the user, an icon 703indicating that the thumbnail image is already selected (hereinafter,referred to as already-selected icon) is attached.

A pointer 704 is controlled by an input device, such as a mouse. It ispossible for a user to select the function in the application andcontrol execution thereof by moving the pointer 704.

A Select-all-images button 705 is a button for selecting all the imageswithin the specified folder. In a case where a user presses down theSelect-all-images button 705, the state is brought about where all theimages within the specified folder are selected (that is, thealready-selected icon 703 is attached). In a case where the user pressesdown the Select-all-images button 705 again in this state, the selectionof all the images is cancelled.

An Automatic layout button 706 is a button for performing the automaticlayout processing. In a case where a user presses down the Automaticlayout button 706, the images selected by the user or a plurality ofimages included within the specified folder is analyzed for the layoutand the automatic layout processing to automatically arrange at least apart of the analyzed images is performed.

A Manual layout button 707 is a button for a user to perform the layoutmanually. Some users desire to perform the layout by themselves in placeof performing the automatic layout processing. In a case where theManual layout button 707 is pressed down, the automatic layoutprocessing is not performed. In this case, the mode is such that a userarranges the images selected by the user him/herself via a GUI. Asdescribed above, it is possible for a user to select the layout creationmethod by pressing down either the Automatic layout button 706 or theManual layout button 707.

In the image selection window 700, a message 708 for notifying a user ofthe number of photos selected by the user and the number of photos thatcan be selected is also displayed.

Returning to the explanation of FIG. 5. At S504, whether or not toperform the automatic layout processing is determined. Specifically,which of the Automatic layout button 706 and the Manual layout button707 is pressed down is determined. In a case where it is determined thatthe Automatic layout button 706 is pressed down, the processing advancesto S505 and on the other hand, in a case where it is determined that theManual layout button 707 is pressed down, the processing advances toS506.

At S505, the automatic layout processing is performed. The “automaticlayout processing” is processing to automatically arrange the imagesselected automatically or selected manually by a user on a double-pagespread based on the preference and tendency of the user. The automaticlayout processing will be described later.

At S506, as a UI of the application, a screen for editing the layout ofthe album (referred to as layout editing UI screen) is displayed. It ispossible for a user to edit the layout via the layout editing UI screen.The user terminal 100 receives a user input via the layout editing UIscreen.

Here, the layout editing UI screen is explained by using FIG. 8. FIG. 8shows an example of the layout editing UI screen. An editing window 800has a display area 801 in which an editing screen of a double-pagespread currently being selected is displayed and a selection area 806for selecting an editing-target double-page spread.

In the display area 801, a preview 802 of the double-page spreadcurrently being edited is displayed. The preview 802 shows results ofthe layout created in a case where images 803 scheduled to be used arearranged.

Buttons 804 and 805 are buttons for changing the double-page spread thatis displayed in the display area 801. It is possible for a user tochange the double-page spread that is displayed in the display area 801to the next double-page spread (in detail, the double-page spread whosepage numbers are just after those of the double-page spread currentlybeing displayed) by pressing down the button 804. In contrast to this,it is possible for a user to change the double-page spread that isdisplayed in the display area 801 to the previous double-page spread (indetail, the double-page spread whose page numbers are just before thoseof the double-page spread currently being displayed) by pressing downthe button 805.

In the selection area 806, as the thumbnail image of each of theplurality of double-page spreads configuring the album, the thumbnailimages of the double-page spread currently being displayed in thedisplay area 801 (that is, the double-page spread currently beingselected) and the other double-page spreads are displayed. In theexample in FIG. 8, as indicated by symbol 807, the double-page spread ofpages 7 and 8 is selected.

In a case where a user presses down an Effect button 820, effectprocessing is performed for the image arranged in the slot selected bythe user on the layout currently being edited. As the effect processingat the time of the pressing down of the Effect button 820, predeterminedprocessing may be performed, or it may also be possible to enable theuser to open another GUI and set the kind of effect via the GUI.

In a case where a user presses down a Correct button 821, correction isperformed for the image arranged in the slot selected by the user on thelayout currently being edited. Here, it is assumed that automatic photocorrection is performed as the predetermined processing at the time ofthe pressing down of the Correct button 821. However, an aspect may beaccepted in which it is possible for the user to open another GUI andset the kind of correction via the GUI.

In a case where a user presses down a Trimming button 822, trimmingprocessing is performed for the image arranged in the slot selected bythe user on the layout currently being edited. In the trimmingprocessing, a main object area is specified based on the image analysisinformation and then trimming is performed so that the main object areabecomes the maximum in the range in which the size of the main objectarea is included in the slot.

A Select-image button 811 is a button for returning to the imageselection again. In a case where a user presses down the Select-imagebutton 811, the image selection window 700 is displayed along with theediting window 800 or in place of the editing window 800. An Orderbutton 812 is a button for terminating the editing work of the album andadvancing to the next ordering work. In a case where the Order button812 is pressed down, the album data and editing history data aretransmitted from the user terminal 100 to the cloud server 110. Theediting history data is used for learning in the cloud server 110. Forexample, in a case where the amount of the editing history after theautomatic layout processing is small, it is possible to regard that thepreference of a user is reflected correctly in the album creationparameter used at the time of the automatic layout processing.

A pointer 808 is controlled by an input device, such as a mouse. It ispossible for a user to perform various kinds of editing work by usingthe pointer 808. For example, in a case where the image 803 the userdesires to move exists, it is possible for the user to select one or aplurality of movement-target images and move the image(s) leftward,rightward, upward, or downward to a desired position by dragging anddropping them with the pointer 808. Further, for example, in a casewhere the image the user desires to correct exists, it is only requiredfor the user to select the target image by using the pointer 808 andpress down the Correct button 821. By pressing down the Correct button821, the correction processing for the target image is performed. Anaspect may be accepted in which it is possible for the user to selectand control image sharpening, brightness correction, saturationcorrection, and the like, via another window that is displayed bypressing down the Correction button 821.

In a case where a user desires to add a new image, it is possible to addan image in the preview 802 currently being selected by displaying theimage selection window 700 again by pressing down the Select-imagebutton 811, and selecting the image and pressing down the Manual layoutbutton 707. As editing items other than the editing items describedpreviously, changing of the template to be used, selection of thebackground color, setting of the background pattern, adjustment of theamount of margin, and the like are considered.

As described above, via the layout editing UI screen that is displayedat S506, the layout editing in accordance with the preference of a useris performed. By acquiring the operation log at this time and using thelog for the learning of the album creation parameter (to be explainedlater in detail), it is made possible to automatically create an albumin accordance with the preference and tendency of a user.

At S507, whether the layout editing is completed, specifically, whetherthe Order button 812 is pressed down is determined. In the presentembodiment, each time the contents relating to one editing item aredetermined, whether the editing is completed is determined at S507. In acase where the determination results at S507 are affirmative, theprocessing advances to S508. On the other hand, in a case where thedetermination results at S507 are negative (that is, in a case where newediting instructions are given), the processing returns to S506 and thelayout editing is continued.

At S508, the created album data is transmitted to the cloud server 110.In a case where the album data is transmitted to the cloud server 110 atthis step, the operation history data on a user in the application isalso transmitted.

At S509, the album ordering processing is performed. The album orderingprocessing includes transmission of the album fee payment completionnotification to the cloud server 110, reception of the album sendingdestination data that is input by a user, and transmission to the cloudserver 110. In a case where all the pieces of processing relating to thealbum ordering in the user terminal 100 are completed, the cloud server110 transfers the album data transmitted from the user terminal 100 atS508 to the PC of the print laboratory 200.

<About Automatic Layout Processing>

In the following, the automatic layout processing (S505 in FIG. 5) isexplained by using FIG. 9. FIG. 9 is a flowchart for explaining detailsof the automatic layout processing performed in the user terminal 100.Here, a case is explained as an example where a layout is created byautomatically selecting images used for the album from an image groupincluded within a folder specified by a user and automatically arrangingthe selected images on a double-page spread.

At S901, an analysis image is created by acquiring an image within afolder specified by a user via the image selection UI screen andperforming re-size processing for the acquired image.

At S902, the acquired album creation parameter is set and based on theset album creation parameter, the image analysis for the analysis imagecreated at S901 is performed. For example, by the analysis at this step,the information on the image capturing date for each image is acquiredand as a result, it is made possible to calculate the difference in theimage capturing time between adjacent images. The item that is analyzedat this step is determined in advance, but it may also be possible tochange the analysis item or the analysis condition in accordance withthe setting of the album creation parameter.

At S903, whether all the images within the specified folder are analyzedis determined. In a case where the determination results at this stepare affirmative, the processing advances to S904. On the other hand, ina case where the determination results at this step are negative, theprocessing returns to S901 in order to perform the image analysis forthe image not analyzed yet.

At S904, by using the analysis results at S902, the main objectdetermination and the scene determination are performed. Here, thenumber of images in which a specific object is included is counted foreach object and the specific object whose count value is the greatest isdetermined to be the main object. As the specific object, for example,person, child, baby, dog, cat, flower, dish, or the like is considered.For the object determination, it is possible to use a conventionaldetermination method, such as the method described in Japanese PatentLaid-Open No. 2017-038343.

In the scene determination, by using the analysis results at S902, whichof the predetermined scenes the scene of an image is classified into isdetermined. In the present embodiment, wedding, journey, and others aretaken as the three predetermined scenes and for each scene, thedistribution of the number of shots and the distribution of the numberof faces are set in advance. The scene of an image is determined bydetermining which of the distributions of these three scenes thedistributions of the scene of the image most resemble. For the scenedetermination, it may also be possible to use a conventionaldetermination method, such as the method described in Japanese PatentLaid-Open No. 2017-038343.

At S905, the image group within the specified folder is divided into subimage groups. The division at this step is performed so that the portionat which the difference in the image capturing time between adjacentimages is large is the boundary and performed repeatedly until thenumber of sub image groups and the number of double-page spreads of thealbum become the same.

The processing at S906 to S910 is performed for each double-page spread.

At S906, the tendency of the layout image within the double-page spreadis determined. Here, explanation is given by taking a case as an examplewhere the tendency is determined in accordance with the main object. Themain object determined at S904 and the main object within the setting ofthe album creation parameter are compared. In a case where the resultsof the comparison indicate that both are different, priority is given tothe main object determined at S904 as the object that is caused toappear in the results of the layout to be created from now. As anexample, a case is considered where the setting is performed within thealbum creation parameter so that a person is given a score higher thanthat given to a pet (for example, dog or cat), but on the other hand,the number of images of the pet is larger than the number of images ofthe person in the specified folder. In the case such as this, thesetting of the album creation parameter is cancelled and the setting ischanged so that the images of the object (in this example, images ofpet) included more in the specified folder are given a high score. Inthe first ordering with no ordering history, it is recommended to usethe default parameter setting. Then, in a case where the tendency of thelayout image within the double-page spread is different from that of thedefault parameter, the setting is changed so that the images of theobject included more in the specified folder are given a high score.Here, for explanation, the method of performing control so that the mainobject is given a high score is described, but it may also be possibleto manage whether an object is included for each manage by attaching aflag and select the image to be used based on the flag.

At S907, the album creation parameter is determined selectively.Specifically, the album creation parameter for each scene is stored inadvance in the user terminal 100 and in accordance with the results ofthe scene determination at S704, the album creation parameter isdetermined. Alternatively, it may also be possible to determine thealbum creation parameter in accordance with the results of the mainobject determination at S704 by storing in advance the album creationparameter for each main object in the user terminal 100.

At S908, scoring for the image is performed. At this step, based on thefeature value derived by the analysis at S902, the score is calculatedfor each image. At the time of score calculation, by changing the weightof the feature value in accordance with the determined scene, thecontrol is performed so that the score is different for each scene. Forexample, in a case where it is determined that the scene of the image isjourney, the setting is performed so that the image whose face size issmall is given a score high compared to that given to the image whoseface size is large. On the other hand, in a case where it is determinedthat the scene of the image is wedding, the setting is performed so thatthe image whose face size is large is given a score high compared tothat given to the image whose face size is small.

At S909, a double-page spread layout is created. In the double-pagespread layout creation at this step, based on the number of image slotsset in the album creation parameter, images to be used are selected. Atthe time of this image selection, it may also be possible to divide theallocated image capturing date section into the number of sectionscorresponding to the number of images to be selected and select an imagefor each divided section.

At S909, layout results using the selected images are created. Here,explanation is given by taking a case as an example where a fixedtemplate with which it is possible to arrange the selected images themost appropriately is selected from among a plurality of fixed templatesalready created in advance and the layout results are created byarranging the images in the selected fixed template. For each number ofimages to be arranged, one or a plurality of fixed templates is createdand in the user terminal 100, a variety of variations of the fixedtemplate are prepared in advance, in which the combination of aspectratios of images, the amount of margin between images, the amount ofmargin of commodity material rim, the way photos overlap, and the likeare changed.

At S909, first, among the plurality of fixed templates prepared inadvance, a fixed template with which it is possible to arrange theselected images is extracted. The fixed template that is extracted hereis specifically a template whose number of images and aspect ratio arethe same. In the extracted template, the images whose score is high arearranged in the slot in order from the slot whose size is the largest.The processing such as this is performed repeatedly the number of timescorresponding to the number of extracted templates. As a result of that,candidate layouts are created so as to correspond to the number ofextracted templates.

After that, one final layout is selected from among the createdcandidate layouts. To explain in detail, layout evaluation is performedfor the one created candidate template or for each of the plurality ofcreated candidate templates. The layout evaluation is performed based onthe point of view of the amount of margin within the double-page spread,the size ratio between the maximum slot and the minimum slot, thetrimming amount of the image, whether the main object is hidden by theslot of the upper layer, whether the main object overlaps a fold, andthe like.

Further, the layout evaluation is performed based on the amount ofmargin included in the album creation parameter, the size ratio betweenthe maximum slot and the minimum slot, and the like. The candidatelayout whose evaluation value is the highest as a result of the layoutevaluation is determined as the final layout. In a case where an item,such as a template number whose use frequency is high, is specified inthe album creation parameter, it may also be possible to preferentiallyuse the template specified by the information. Further, for example, ina case where an item of the connection tendency of successive templatesis specified in the album creation parameter, it may also be possible tocreate the final layout after determining the template of eachdouble-page spread based on the information. The item of the connectiontendency of successive templates is an item indicating whether there isa tendency for a specific template to follow a certain template.

At S910, whether the layouts corresponding to all the double-pagespreads are created is determined. In a case where the determinationresults at this step are affirmative, the automatic layout processing isterminated (that is, the processing advances to S906). On the otherhand, in a case where the determination results at this step arenegative, the processing returns to S906 and the automatic layoutprocessing is continued until the layouts corresponding to all thedouble-page spreads are created.

<About General Flow of Learning Phase in Cloud Server>

In the following, the learning processing performed in the cloud server110, specifically, the general flow of the phase of learning the albumcreation parameter based on album data that is transmitted to the cloudserver 110 at the time of ordering is explained by using FIG. 10.

At S1001, the cloud server 110 having stood by receives the completionnotification of the alum ordering processing, which is transmitted fromthe user terminal 100.

At S1002, the album data corresponding to the completion notificationreceived at S1001 is acquired. In the album data acquired at this step,UserID for identifying a user who has created the album data (albumcreator) is included.

At S1003, based on the album data (layout data) acquired at S1002, thealbum feature value is calculated.

At S1004, the album feature value calculated at S1003 is saved in theHDD 115.

At S1005, whether or not to perform learning is determined. First, thealbum feature value of the album ordered in the past by the user of theUserID acquired at S1002 is acquired and the acquired feature value andthe album feature value saved in the HDD 115 at S1004 are compared. Inthis comparison, the album feature value is regarded as being a vectorand a correlation is taken in a round-robin manner so as to correspondto the number of albums ordered in the past. In a case where thecalculated correlation value is greater than or equal to a predeterminedthreshold value, it is determined that there is a relationship and onthe other hand, in a case where the calculated correlation value is lessthan the predetermined threshold value, it is determined that there isno relationship. In a case where there is at least one album featurevalue for which it is determined that there is a relationship, thepossibility that the ordering is made again by making use of the albumdata ordered in the past again is high, and therefore, learning is notperformed and the processing returns to S1001 and stands by. In contrastto this, in a case where there is no album feature value for which it isdetermined that there is a relationship, the processing advances toS1006 and the learning using the album feature value is performed.

At S1006, based on the UserID acquired at S1002, the album feature valueof the learning-target UserID is read from the HDD 115. At this time,the number of acquired album feature values is counted. In the HDD 115,the past ordering data is stored. At this step, it may also be possibleto select the album feature value used for the leaning based on thealbum ordering interval. For example, in a case where the orderinginterval of a user who ordered an album is longer than or equal to threeyears, it is considered that the album feature value of the albumordered in the past is not used for the learning. Alternatively, in acase where an album is ordered the very most recently (that is,continuously by the same user), it may also be possible to use only thealbum feature value of the album ordered in the past three months forthe learning. Further, it may also be possible not to use the dataordered again for the learning.

At S1007, whether to perform learning by using only the album featurevalue of a single user (that is, the user of the UserID acquired atS1002) is determined. In a case where the determination results at thisstep are affirmative, the processing advances to S1009, and on the otherhand, in a case where the determination results are negative, theprocessing advances to S1008. In this example, the determination basedon the number of album feature values counted at S1006 is performed.Specifically, in a case where the number of album feature values islarger than or equal to a predetermined threshold value, it isdetermined that learning is performed by using only the album featurevalue of a single user and the processing advances to S1009. On theother hand, in a case where the number of album feature values is lessthan the predetermined threshold value, the processing advances toS1008.

At S1008, GroupID for identifying a group to which the user of theUserID acquired at S1002 belongs is acquired and all the album featurevalues associated with the acquired GroupID are read from the HDD 115.The GroupID is for managing the users whose preference is similar,including other users, for each group. In a case where it is determinedthat number of album feature values of a single user is insufficient atS1007, the learning is performed by adding the album feature values ofother users. However, at that time, the album feature values of all theusers, which are saved in the HDD 115, are not taken as the data to beadded and the album feature values of the users in the same line areselected and made use of as the learning data. In the presentembodiment, for the purpose of determining whether the album featurevalues are those of the users in the same line, the GroupID foridentifying the group to which the user belongs is allocated to eachalbum.

Here, the allocation of the GroupID, which is performed in the cloudserver 110, is explained by using FIG. 11.

At S1101, the cloud server 110 in the state of waiting for modificationof the GroupID receives the completion notification of the albumordering processing, which is transmitted from the user terminal 100.

At S1102, the album feature values of all the UserIDs, which are thefeature values of all the albums, are read from the HDD 115.

At S1103, clustering of the GroupID is performed. The clustering isperformed by plotting all the acquired album feature values in amulti-dimensional feature value space. To be more detail, the distancebetween two album feature values in the feature value space iscalculated and in a case where the calculated distance is less than orequal to a predetermined threshold value, it is determined that the twoalbums is in the same class. For all the album feature values, thedistances are calculated in a round-robin manner, and a class isallocated, which has, as its center, the album feature value whosenumber is the largest among the album feature values for which thealbums are determined to be in the same class. The processing such asthis is repeated until a belonging class is allocated to all the albumfeature values. As a result, each album feature value is allocated toone of the classes.

At S1104, the GroupID and the UserID are associated with each other.Based on the results of the clustering at S1103, the class allocated toeach album feature value is allocated as the GroupID.

At S1105, modification of the GroupID is performed for all the albumfeature values. The reason the processing at this step is performed isthat it is necessary to update the GroupID attached to the saved albumfeature value by taking into consideration the added album feature valuebecause the album feature value is added by the ordering.

Here, the method of allocating the GroupID by clustering is described,but it may also be possible to allocate the UserID as the same class,for which the possibility that the preference is the same is high basedon, such as an age group and sex, by using the user profile associatedwith the UserID. That is, it may also be possible to allocate theGroupID by making use of one other than the similar feature value. Forthe users whose user profile, such as sex and age, is similar, thepossibility that the preference is the same is high, and therefore, thesame GroupID is allocated. Further, in a case where images are managedin the cloud server 110 or the like, it may also be possible to allocatethe same GroupID based on the kind of object included in the mostrecently uploaded image.

Further, it may also be possible to classify the album feature valuesinto categories. For example, the album feature values are classifiedinto categories based on the scene determined by the scenedetermination. Specifically, in a case where the album feature valuesare classified into three scenes, that is, wedding, journey, and others,it is recommended to classify the album feature values into threecategories of the album feature value for wedding, the album featurevalue for journey, and the album feature value for others. The albumcreation parameters are also classified into a plurality of categoriesaccordingly.

Explanation is returned to FIG. 10. At S1009, the learning is performedbased on the album feature values acquired at S1006 and S1008. Detailsof the learning processing will be described later.

At S1010, the album creation parameter is updated. In detail, the albumcreation parameter is updated by the album creation parameter generatedby an album creation parameter generation unit 1213 (see FIG. 12), to bedescribed later.

At S1011, the ID for the album ordered this time and the album creationparameter are associated with each other.

In a case where the application is activated in the user terminal 100,at S1012, the cloud server 110 transmits the album creation parameterfor which learning has been performed to the user terminal 100 (downloadof the album creation parameter for which learning has already beenperformed). In the user terminal 100, updating of the album creationparameter using the downloaded album creation parameter for whichlearning has already been performed is performed. Due to this, it ismade possible to use the album creation parameter for which learning hasalready been performed in each module of the application, and as aresult, the setting of the updated parameter is reflected at the time ofthe automatic layout processing.

<About Learning for Finding Album Creation Parameter in Accordance withPreference of User>

In the following, as the learning method of the present embodiment, amethod of generating an album creation parameter 1204 in accordance withthe preference of a user based on a user album feature value 1206 isexplained by using FIG. 12.

An image set 1201 is an image set used for learning. As an image to beincluded in the image set used for learning, it is possible to use animage published on a WEB, an image prepared by an album serviceprovision company, and the like, not only an image captured by a user.

An image setting unit 1212 creates the image set 1201 by using an imagesaved in a usable image DB (in the this example, the HDD 115). In thepresent embodiment, as the image set 1201, two kinds of image set, thatis, a learning image set and a verification image set are created.

In a case where the automatic layout processing is performed for theimage set 1201 in an automatic layout processing unit 1210 by using thealbum creation parameter 1204, album data 1202 (layout data) is created.The album creation parameter 1204 is a parameter that is optimized byrepeating learning. The album creation parameter generation unit 1213generates the album creation parameter necessary at the time ofperforming the automatic layout processing in the automatic layoutprocessing unit 1210. The automatic layout processing unit 1210 is amodule that provides the automatic layout function described previously.

An album feature value acquisition unit 1211 derives (calculates) analbum feature value 1203 based on the album data 1202 generated by theautomatic layout processing unit 1210.

In FIG. 12, the user album feature value 1206 is a feature valueindicating the feature of the album created by a user. A target albumfeature value estimation unit 1216 estimates a target album featurevalue based on the user album feature value 1206. The target albumfeature value estimation unit 1216 also acquires a correct album featurevalue (referred to as correct target album feature value) 1207 of thetarget album from the user album feature value 1206. The processing ofthe target album feature value estimation unit 1216 will be describedlater by using FIG. 13.

A learning control unit 1214 controls learning work that is explained inthe present embodiment. An album feature value comparison unit 1215compares the album feature value 1203 and the correct target albumfeature value 1207. The comparison by the album feature value comparisonunit 1215 and the estimation processing of the target album featurevalue by the target album feature value estimation unit 1216 areperformed repeatedly (that is, learning is performed repeatedly). Due tothis, it is possible to find a target album feature value 1205 puttingthe preference of a user together.

<About Estimation of Target Album Feature Value>

In the following, details of the target album feature value estimationunit 1216 in FIG. 12 are explained by using FIG. 13. FIG. 13 is adetailed functional block diagram of the target album feature valueestimation unit 1216.

An album feature value acquisition unit 1301 derives an album featurevalue based on the album data. Here, the album feature value acquisitionunit 1301 acquires the user album feature value 1206 and the correcttarget album feature value 1207. By the user album feature value 1206being input to a learning unit 1304, such as DeepLearning, the targetalbum feature value 1205 is output. The correct target album featurevalue 1207 is delivered to the album feature value comparison unit 1215as it is. The user album feature value 1206 and the correct target albumfeature value 1207 are allocated for learning and for verification,respectively. The “verification” is a piece of processing in thelearning phase and details will be explained by using FIG. 14.

A learning data setting unit 1302 allocates and sets the album featurevalue for learning, which is used at the time of learning, of the useralbum feature value 1206. The album feature values corresponding to thenumber, which is a predetermined percentage of the total number ofacquired album feature values, are set as the learning data. At thistime, in a case where the number of pieces of learning data is less thana predetermined number, the feature value in units of album double-pagespreads is set as the learning data. On the other hand, in a case wherethe number of pieces of learning data is larger than or equal to thepredetermined number, the feature value in units of albums is set as thelearning data.

A verification data setting unit 1303 sets the album feature valuesexcept for those set as the learning data by the learning data settingunit 1302 among the album feature values acquired at S1006 as theverification data. At this time, in a case where the feature value inunits of double-page spreads is set as the learning data in accordancewith the number of pieces of learning data in the learning data settingunit 1302, as the verification data also, the feature value in units ofdouble-page spreads is set. On the other hand, in a case where thefeature value in units of albums is set as the learning data, as theverification data also, the feature value in units of albums is set.

The learning unit 1304 performs estimation processing to output thetarget album feature value 1205 by using the user album feature value1206. It is possible for the learning unit 1304 to switch the learningmethod to be adopted in accordance with the number of pieces of learningdata. In a case where the number of pieces of learning data is largerthan or equal to a predetermined value, the learning by the machinelearning is performed. Here, as the algorithm of the machine learning,DeepLearning is used. The DeepLearning configures a network in which thefeature values whose number of dimensions is the same size as the numberof dimensions of the album feature values are estimated. It may also bepossible to perform learning that takes into consideration the timeseries by adding the date of the album ordering data. Further, it mayalso be possible to use the machine learning, such as SVM, in place ofthe DeepLearning.

On the other hand, in a case where the number of pieces of learning datais less than the predetermined value, the learning by a rule base isperformed. It may also be possible to perform a main component analysis,such as an average and a mode, by using a statistical method.Alternatively, it may also be possible to select results whose distancesare close in a feature value space by performing plotting in the featurevalue space.

As a result of performing the estimation processing by using thelearning data, the target album feature value 1205 is estimated. To thetarget album feature value estimation unit 1216, the comparison resultsby the album feature value comparison unit 1215 are input from thelearning control unit 1214, and therefore, the estimation accuracy ofthe target album feature value 1205 is improved. This series of flowwill be explained by using FIG. 14.

Here, for explanation, the method of learning the fixed album featurevalue is described, but the items of the learning-target album featurevalue may be variable. For example, in a case where the number of timesof ordering is small, the number of pieces of learning data is small,and therefore, it may also be possible to perform learning by reducingthe number of learning items. In a case where the number of times ofordering increases and the number of pieces of learning data increases,it may also be possible to increase the number of learning items. Thatis, the number of learning items in a case where the number of pieces oflearning data is large is set larger than the number of learning itemsin a case where the number of pieces of learning data is small. Further,in a case where the learning does not converge successfully, it may alsobe possible to reduce the number of learning items. In a case where thelearning converges, it may also be possible to increase the number oflearning items.

In a case where the album creation parameter for which learning hasalready been performed exists at the time of performing learning, it mayalso be possible to perform additional learning by setting the albumcreation parameter for which learning has already been performed as theinitial value. In a case where the number of pieces of data to be usedfor learning is small, it may also be possible to increase the number oftimes of learning by changing the combination of the learning data andthe verification data.

The learning control unit 1214 determines whether or not to terminatethe learning (S1408 and S1414 in FIG. 14, to be described later). In acase where the correlation value calculated in the processing process inthe learning unit 1304 is greater than or equal to a predeterminedthreshold value, it is preferable to determine to terminate thelearning. In a case where the correlation value is less than thepredetermined threshold value, the processing is continued. At thistime, in a case where the correlation value is less than a predeterminedvalue, it may also be possible to limit the updating of the albumcreation parameter, to be described later, from being performed.

<About Learning Processing>

In the following, the learning processing (see FIG. 12, FIG. 13)described previously is explained by using FIG. 14. The learningprocessing in FIG. 14 includes a learning loop and a verification loop.

In a case where the learning of the album creation parameter 1204 isstarted, at S1401, the learning control unit 1214 instructs the targetalbum feature value estimation unit 1216 to perform estimation of thetarget album feature value 1205 and acquisition of the correct targetalbum feature value 1207.

The target album feature value estimation unit 1216 having received theinstructions calculates (estimates) the target album feature value 1205based on the already-acquired user album feature value 1206. Here, bythe album feature value of the learning data set in the learning datasetting unit 1302 in FIG. 13 being input to the learning unit 1304, thetarget album feature value 1205 is estimated. The estimated target albumfeature value 1205 is delivered to the album creation parametergeneration unit 1213 via the learning control unit 1214. For example,based on the user album feature value 1206 of the layout data on thefirst double-page spread of the album generated by a user and the useralbum feature value 1206 of the layout data on the second double-pagespread, the target album feature value is estimated. The estimatedtarget album feature value 1205 is output to the album creationparameter generation unit 1213 via the learning control unit 1214.

Further, the target album feature value estimation unit 1216 acquiresthe correct target album feature value 1207 based on the instructionsfrom the learning control unit 1214 and delivers the acquired correcttarget album feature value 1207 to the album feature value comparisonunit 1215. For example, the target album feature value estimation unit1216 acquires the album feature value of the layout data on the thirddouble-page spread of the album generated by a user as the correcttarget album feature value 1207 and delivers the correct target albumfeature value 1207 to the album feature value comparison unit 1215.

At S1402, the learning control unit 1214 instructs the album creationparameter generation unit 1213 to generate the album creation parameter1204. Upon receipt of the instructions, the album creation parametergeneration unit 1213 converts the target album feature value 1205 outputfrom the target album feature value estimation unit 1216 into the albumcreation parameter 1204. For example, based on the target album featurevalue 1205 estimated based on the user album feature value 1206 of thefirst double-page spread and the second double-page spread, the albumcreation parameter 1204 is generated. At the time of performing learningfor the first time, it may also be possible to use a predeterminedparameter as the initial album creation parameter, or newly create aparameter obtained by randomly changing the parameter.

At S1403, the learning control unit 1214 instructs the image settingunit 1212 to set the image set 1201 for learning. In response to theinstructions, the image setting unit 1212 inputs the image set 1201 forlearning to the automatic layout processing unit 1210. For example, aplurality of images used in the layout data on the third double-pagespread is input to the automatic layout processing unit 1210 as theimage set 1201.

At S1404, the automatic layout processing unit 1210 creates the albumdata 1202 (layout data) by using the album creation parameter 1204generated at S1402 and the image set 1201 for learning, which is set atS1403. For example, the album data 1202 is generated by using the albumcreation parameter 1204 based on the target album feature value 1205estimated based on the first double-page spread and the seconddouble-page spread, which is generated at S1402, and the images of thethird double-page spread. In this case, the generated album data canalso be said as the estimated album data on the third double-page spread(estimated layout data on the third double-page spread). The automaticlayout processing is already explained, and therefore, explanation isomitted (see FIG. 9).

At S1405, the album feature value acquisition unit 1211 acquires thealbum feature value 1203 corresponding to the album data 1202 created atS1404. For example, the album feature value of the estimated album dataon the third double-page spread is acquired. In this case, the acquiredalbum feature value can also be said as the estimated album featurevalue of the third double-page spread.

At S1406, the album feature value comparison unit 1215 compares thecorrect target album feature value 1207 acquired at S1401 and the albumfeature value 1203 acquired at S1405. For example, the correct targetalbum feature value 1207 of the third double-page spread, which isacquired at S1406, and the estimated album feature value of the thirddouble-page spread are compared.

The information based on the comparison results is delivered to thealbum creation parameter generation unit 1213 via the learning controlunit 1214. Then, based on the information based on the comparisonresults, the album creation parameter 1204 is updated so that theestimated album feature value 1203 becomes closer to the correct targetalbum feature value 1207. The information based on the updating resultsof the album creation parameter 1204 is input to the target albumfeature value estimation unit 1216 via the learning control unit 1214.Then, the parameter (for example, the weighting parameter between eachnode of the neural network) in the learning unit 1304 of the targetalbum feature value estimation unit 1216 is optimized.

The information based on the comparison results may be delivereddirectly to the target album feature value estimation unit 1216 via thelearning control unit 1214. Then, the parameter (for example, theweighting parameter between each node of the neural network) in thelearning unit 1304 of the target album feature value estimation unit1216 may be optimized. In this case, the information based on thecomparison results may not be input to the album creation parametergeneration unit 1213. In a case where the estimation accuracy of thetarget album feature value 1205 is improved by the target album featurevalue estimation unit 1216, the album creation parameter generated fromthe target album feature value 1205 is optimized naturally.

As described above, by the learning due to the estimation by the targetalbum feature value estimation unit 1216 and the comparison by the albumfeature value comparison unit 1215, the optimization of the albumcreation parameter and the improvement of the estimation accuracy of thetarget album feature value 1205 are implemented.

At S1407, the learning control unit 1214 determines whether the learningloop is performed a predetermined number of times. In a case where thedetermination results at this step are affirmative, the processingadvances to S1408 and on the other hand, in a case where thedetermination results are negative, the processing returns to S1402. Thepredetermined number of times, which is adopted at this step, does notneed to be a particularly determined value.

By the processing explained above, it is possible to perform learning sothat the estimated album feature value 1203, which is obtained as aresult of performing the automatic layout processing while updating theimage set 1201 and the album creation parameter 1204, becomes closer tothe correct target album feature value 1207.

At S1408, based on the comparison results between the correct targetalbum feature value 1207 at that point in time and the estimated albumfeature value 1203, whether or not to complete the learning loop isdetermined. For example, whether or not the comparison results at S1406indicate a predetermined correct percentage is determined. In a casewhere the correct target album feature value 1207 and the estimatedalbum feature value 1203 are not the feature values close sufficiently(that is, in a case where the correct percentage is low), the processingreturns to S1402 in order to continue the learning loop. On the otherhand, in a case where the correct target album feature value 1207 andthe album feature value 1203 are the feature values close sufficiently(that is, in a case where the correct percentage is high), the learningloop is terminated and the processing advances to S1409.

The flow so far is explained again by using a specific example. Forexample, it is assumed that there are user album feature valuescorresponding to three double-page spreads. The target album featurevalue 1205 is calculated by the learning unit 1304 using the firstdouble-page spread and the second double-page spread among them. Basedon the calculated target album feature value, the album creationparameter 1204 is generated by the album creation parameter generationunit 1213. Here, the image setting unit 1212 sets the images used on thethird double-page spread that is not used among the three double-pagespreads as the image set 1201. Then, the automatic layout processingunit 1210 performs the automatic layout processing for the set image set1201 of the third double-page spread by using the generated albumcreation parameter. After that, the album feature value acquisition unit1211 acquires the album feature value (estimated album feature value ofthe third double-page spread) corresponding to the created album data(estimated album data on the third double-page spread). This estimatedalbum feature value is the estimated album feature value of the thirddouble-page spread, which is created by using the album creationparameter estimated based on the first double-page spread and the seconddouble-page spread. Then, the estimated album feature value and thecorrect target album feature value of the third double-page spread arecompared by the album feature value comparison unit 1215. Due to this,it is possible to check the estimation accuracy of the target albumfeature value estimation unit 1216.

As described above, in a case where the number of pieces of learningdata is small, the feature value in units of the album double-pagespreads is set as the learning data and in a case where the number ofpieces of learning data is large, the feature value in units of albums(units of photo books including a plurality of double-page spreads) isset as the learning data. For example, the album creation parameter iscreated based on the first album and the second album and the automaticlayout is performed by using the created album creation parameter andthe images used for the third album. Then, the estimated album featurevalue of the third estimated album data generated as a result of thelayout and the third correct target album feature value are compared.

By reflecting the comparison results in the learning unit 1304 in FIG.13, the estimation accuracy of the album creation parameter is improved.In a case where the estimation accuracy is low, it is possible toimprove the estimation accuracy by performing estimation repeatedlywhile changing the setting of the learning. Due to this, the albumcreation parameter in which the preference of a user is reflected to acertain extent is generated. In order to verify whether the albumcreation parameter is really accurate, in the learning phase of thepresent embodiment, the verification flow is performed. In FIG. 14, thelearning loop and the verification loop (verification flow) aredescribed separately, but the verification loop is also one flow in thelearning.

In the following, the verification loop in the learning phase isexplained. At S1409, the learning control unit 1214 instructs the imagesetting unit 1212 to set the image set 1201 for verification, which isnot used in the learning loop so far. For example, in a case where thefirst double-page spread to the eighth double-page spread are used inthe learning loop, in the verification loop, the ninth and subsequentdouble-page spreads are used.

At S1410, the automatic layout processing unit 1210 creates the albumdata 1202 by using the album creation parameter 1204 generated by thelearning loop and the image set 1201 for verification, which is set atS1409. For example, by using the album creation parameter 1204 generatedby the learning loop and the images used on the ninth double-pagespread, the album data is generated. This album data can also be said asthe estimated album data on the ninth double-page spread, which isestimated by using the album creation parameter.

At S1411, the album feature value acquisition unit 1211 acquires thealbum feature value 1203 corresponding to the album data 1202. Forexample, the album feature value of the estimated album data on theninth double-page spread (estimated album feature value of the ninthdouble-page spread) is acquired.

At S1412, the album feature value comparison unit 1215 compares thecorrect target album feature value 1207 and the estimated album featurevalue 1203 acquired at S1411. For example, the album feature value ofthe album data on the ninth double-page spread (correct target albumfeature value of the ninth double-page spread) created actually by auser and the estimated album feature value of the ninth double-pagespread are compared. Due to this, it is possible to verify whether thegenerated album creation parameter is accurate by using the ninthdouble-page spread.

At S1413, the learning control unit 1214 determines whether theverification loop is performed a predetermined number of times. In acase where the determination results at this step are affirmative, theprocessing advances to S1414 and on the other hand, in a case where thedetermination results are negative, the processing returns to S1409. Thepredetermined number of times adopted at this step does not need to be aparticularly determined value and may be smaller the number of times ofthe learning loop.

By performing the verification loop at S1409 to S1412, the performanceof the album creation parameter 1204 for which learning has beenperformed is verified. For example, in a case where the correct targetalbum feature value of the ninth double-page spread and the estimatedalbum feature value of the ninth double-page spread reach apredetermined correlation value, it is possible to determine that thepreference of a user is reflected.

As described above, even in a case where the image set 1201 forverification is set, which is different from the image set 1201 forlearning, on a condition that the album feature value 1203 is close tothe target album feature value 1205, it is determined that theverification is completed at S1414 and the learning terminates. At thetime of verification, the user album feature value that is set in theverification data setting unit 1303 in FIG. 13 (for example, the albumfeature value of the ninth or subsequent double-page spread) is used.This album feature value is not used at the time of learning. By usingthe album feature value not used for the learning, it is possible toverify whether an album that reflects the preference of a user can becreated by the generated album creation parameter. Specifically, layoutresults are generated in accordance with the images used on thedouble-page spread corresponding to the user album feature value forverification and the album creation parameter created by using the useralbum feature value for learning, and the estimated album feature valuecorresponding to the layout results is derived. By comparing this withthe correct target album feature value for verification, it is possibleto verify whether the preference of a user is reflected. That is, it ispossible to determine whether the album creation parameter satisfies apredetermined condition. Then, in a case where this condition issatisfied, the album creation parameter becomes downloadable at the timeof updating of the application.

In the verification loop also, as in the learning loop, in a case wherethe number of pieces of verification data is small, the feature value inunits of album double-page spreads is set as the verification data, butin a case where the number of pieces of verification data is large, thefeature value in units of albums (in units of photo books) is set as theverification data.

In a case where the difference between the target album feature value1205 and the album feature value 1203 does not become small after theverification, the processing advances to S1402 and the learning isperformed again. By doing so, it is made possible to find the albumcreation parameter 1204 reflecting the preference of a user by learning.

Although not shown in FIG. 14, in order to prepare for a case where itis not possible to create the desired album creation parameter 1204 evenby performing the learning loop and the verification loop, it may alsobe possible to take steps to stop the learning and so on as needed bydetermining in advance the limit number of times of loop. Further, in acase where the learning does not converge, it may also be possible toperform learning under a different condition by changing the setting ofthe target album feature value estimation unit 1216 and so on.

<About Setting Method of Learning Data and Verification Data>

In the following, the setting method of learning data and verificationdata is explained by using FIG. 15A and FIG. 15B.

FIG. 15A shows the setting method of learning data and verification datain a case where learning is performed in units of double-page spreads.In a case where the number of pieces of data that can be made use of forleaning is insufficient, the learning is performed by making use of thedata in units of double-page spreads. For example, in a case where thetotal number of pieces of the double-page spread layout data is n, mpieces of the data are allocated as learning data and k pieces of dataare allocated as verification data (here, n=m+k).

Symbol 1501 indicates the feature value for each double-page spreadnumber. In the feature value, within-double-page-spread information,which is included in album-specific information, is set (see FIG. 16B).In the example in FIG. 15A, it is assumed that there are n featurevalues corresponding to n double-page spreads.

Symbol 1502 indicates an allocated learning data group. Among the ndouble-page spread feature values, m double-page spread feature valuesare allocated for learning. The selection of the m double-page spreadfeature values may be random and here, for explanation, by taking intoconsideration the page configuration of the album, the double-pagespread feature values whose double-page spread numbers are #1 to #n−2are set as the learning data.

Symbol 1503 indicates an allocated verification data group. Among thedouble-page spread feature values (total number is n), those (kdouble-page spread feature values) except for the (m) double-page spreadfeature values set as the learning data are set as the verificationdata. Here, for explanation, the double-page spread feature values whosedouble-page spread number is #n−1 to “#n are set as the verificationdata. It may also be possible to perform division into the learning dataand the verification data for each slot size of the double-page spread.

FIG. 15B shows the setting method of learning data and verification datain a case where the learning is performed in units of albums (in unitsof books including a plurality of spread pages). In a case where thenumber of pieces of album data is n, m pieces of album data areallocated as the learning album data and k pieces of album data areallocated as the verification album data (here, n=m+k).

Symbol 1504 indicates the feature value for each album number. In thefeature value, album entire information on each album andwithin-double-page-spread information are set (see FIG. 16B). In theexample in FIG. 15B, it is assumed that there are n feature valuescorresponding to n albums.

Symbol 1505 indicates an allocated learning album data group. Among then album feature values, m album feature values are allocated forlearning. Selection of the m album feature values may be random andhere, for explanation, the album feature values whose album numbers areno. 1 to no. n−2 are set as the learning data.

Symbol 1506 indicates an allocated verification album data group. Amongthe album feature values (total number is n), those (k album featurevalues) except for the (m) album feature values set as the learning dataare set as the verification data. Here, for explanation, the albumfeature values whose album numbers are no. n−1 to no. n are set as theverification data. It may also be possible to perform division into thelearning data and the verification data for each scene determined at thetime of analysis.

<About Album Creation Parameter>

In the following, the album creation parameter is explained by usingFIG. 16A to FIG. 16C. FIG. 16A is a diagram showing the data structureof the album creation parameter. As shown in FIG. 16A, the albumcreation parameter includes User-specific information and album-specificinformation.

FIG. 16B is a diagram showing each configuration of the User-specificinformation and the album-specific information. GroupID is allocated foreach user group putting together users whose preference is similar. AsUserID, a specific ID is allocated for each user who ordered an album.Further, in the User-specific information, as other pieces ofinformation, Sex, Age, Address, Telephone number, E-mail address, andthe like of a user who ordered an album are stored. The album-specificinformation includes album entire information andwithin-double-page-spread information.

FIG. 16C is a diagram showing each configuration of the album entireinformation and the within-double-page-spread information. In thefollowing, the components (each item) of the album entire informationare explained.

As AlbumID, a specific ID is allocated for each ordered album. InCommodity material size, the commodity material size of the orderedalbum is set. In Type of bookbinding, the type of bookbinding (fullflat, perfect binding, and the like) of the ordered album is set. InMedia, the media kind (glossy paper, semi-glossy paper, and the like) ofthe ordered album is set. In Number of double-page spreads, the numberof pages of the ordered album is set. In Unit price, the price perordered album is set. In Campaign, information relating to whether ornot the album ordered by a user is a campaign-target product andinformation relating to the corresponding campaign are set. In Averagemargin amount, a value obtained by dividing the total margin area withineach double-page spread by the number of double-page spreads is set. InAverage number of image slots, a value obtained by dividing the numberof images used for the album by the number of double-page spreads isset. In Average slot size ratio, a value obtained by dividing the totalslot area within the album by the total number of slots is set. InEntire composition pattern, the pattern (Hinomaru (the national flag ofJapan), tripartition, sandwich, and the like) of the composition ofimages used in the album is set. In Object weight, the ratio relating tothe weight is set for the item relating to the object evaluation at thetime of scoring and the item relating to the quality of image, such ashue and brightness. In Editing operation log, the contents that are laidout and edited in units of double-page spreads are set. The logs, suchas the number of times of image replacement, the number of times ofimage trimming, the kind of correction performed, and the kind of effectperformed, are stored. As the items in the album entire information,information relating to the entire album other than those describedpreviously are stored.

In the following, the components (items) of thewithin-double-page-spread information are explained.

In Double-page spread number, the number of the double-page spreadswithin the ordered album is set. In Margin amount, the area of themargin within the double-page spread specified by Double-page spreadnumber is set. Margin amount may be the width size of the double-pagespread of the rim of the double-page spread or the width size betweenslots within the double-page spread. In Number of image slots, thenumber of image slots arranged on the double-page spread specified byDouble-page spread number is set. In Slot size ratio, the ratio betweenthe size of the maximum slot and the size of the minimum slot among theslots within the double-page spread specified by Double-page spreadnumber is set. In Composition pattern, the composition pattern (Hinomaru(the national flag of Japan), tripartition, sandwich, and the like) ofthe images arranged on the double-page spread specified by Double-pagespread number is stored. In Editing operation log, history informationon editing performed in the layout editing (the number of times of imagereplacement, the number of trimmed slots, and the like) is set. Althoughnot shown schematically, in the items in the within-double-page-spreadinformation, information relating to the double-page spread other thanthose described previously is stored. For example, there are items asfollows.

In Large slot object, the kind of object included in the image arrangedin the large slot is set. In Effect, the kind of effect processingperformed for the layout is set and for example, the kind of effect,such as PhotoOnPhoto and gradation processing, is set. In Number oflayers, the number of layers within the double-page spread is set. InUpper layer object, the object information included in the imagearranged in the slot of the uppermost layer is stored. In Large slotcomposition, the composition information on the image arranged in themaximum slot is set. In Upper layer composition, the compositioninformation on the image arranged in the uppermost layer is set. InTemplate number, the fixed template number used for the double-pagespread is set. In a case where a user creates a template by editing ornewly, a predetermined value is set. In Page straddle, whether or notthe fold of the double-page spread is straddled is set. In Trimmingamount, the trimming amount of each slot is set.

The same item may be included in the album entire information and thewithin-double-page-spread information. It may also be possible to changethe priority of the same item depending on the learning data amount. Forexample, in a case where the learning data amount is small, priority isgiven to the learning of the item set in the album entire information.In a case where the learning data amount is large, priority is given tothe learning of the item set in the within-double-page-spreadinformation. It may also be possible to determine whether or not to addto the learning data in accordance with the campaign information withinthe album entire information.

<About Verification of Album Creation Parameter>

In the following, a method of verifying the album creation parameter setby learning in the user environment is explained by using FIG. 17. FIG.17 shows a case where the album creation parameter is verified on thelayout editing UI screen (see FIG. 8).

It is possible to exchange images, change the image size and so on byperforming the operation by using a mouse on the layout of thedouble-page spread currently being edited.

Symbol 1701 indicates the thumbnail of the layout of pages 1 and 2 inthe album currently being edited. Here, “a” attached to this layoutindicates the kind of album creation parameter at the time of creationof the thumbnail. Similarly, symbol 1702 indicates the thumbnail of thelayout of pages 3 and 4 in the album currently being edited. Here, “b”attached to this thumbnail indicates the kind of album creationparameter (different from the album creation parameter of pages 1 and 2)at the time of creation of the layout. In the example in FIG. 17, thethumbnails of the layouts created by using the two kinds of albumcreation parameter are displayed alternately, but there may be three ormore kinds of album creation parameter.

The automatic layout processing is performed with a plurality of albumcreation parameters and the layout results for each album creationparameter are acquired in advance. In a case where there is an orderinghistory, the kinds of album creation parameter may be two kinds of albumcreation parameter, that is, the album creation parameter for whichlearning has already been performed and the default album creationparameter. In a case where there is no ordering history, the kinds ofalbum creation parameter may be two kinds of album creation parameter,that is, the default album creation parameter and the album creationparameter created in accordance with the results of analyzing the imageat the time of creating the album. For example, in a case where it ismade clear that there are many person images in the candidate images asa result of analyzing the images, the album creation parameter is setwith which a person image is selected preferentially and arranged in theslot whose size is large. It may also be possible to randomly determinewhich album creation parameter to use for each double-page spread, ordetermine the album creation parameter in turn so that the order formsthe shape of a loop.

As described above, by presenting the results of the automatic layoutperformed with a plurality of album creation parameters and acquiringthe history of editing performed by a user for each of the layoutresults, it is possible to verify each album creation parameter. Forexample, in the case in FIG. 17, on a condition that the number of timesof image replacement is smaller for the results of the layout generatedwith parameter “a” than that for the results of the layout generatedwith parameter “b, it is possible to determine that parameter “b”represents the preference of a user more accurately. Further, in a casewhere parameter “b” is the latest album creation parameter, it ispossible to determine that the learning is in progress successfully.

At the time of album ordering, the history of editing by a user is alsouploaded to the cloud server, and therefore, it is possible to select,classify and so on the learning data based on the history.

OTHER EMBODIMENTS

In the embodiment described previously, the method of performinglearning by using the album feature value, but it may also be possibleto design a network in which data obtained by putting together theimages used for the album and the arrangement information is taken as aninput and the album creation parameter is output.

Embodiment(s) of the present disclosure can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

According to the present disclosure, it is made possible to present alayout that reflects the preference of a user.

While the present disclosure has been described with reference toexemplary embodiments, it is to be understood that the disclosure is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2019-107509, filed Jun. 7, 2019, which is hereby incorporated byreference wherein in its entirety.

What is claimed is:
 1. A control method comprising: outputting anestimated feature value based on first layout data generated by a user;generating an album creation parameter based on the output estimatedfeature value; generating layout data based on the generated albumcreation parameter and an image used for second layout data generated bya user; updating the album creation parameter based on a feature valuebased on the generated layout data and a feature value based on thesecond layout data; and performing verification of the updated albumcreation parameter.
 2. The control method according to claim 1, whereinby a first feature value based on the first layout data being input to alearning unit as input data, the estimated feature value is output fromthe learning unit.
 3. The control method according to claim 2, whereinbased on comparison results between a feature value based on thegenerated layout data and a feature value based on the second layoutdata, the parameter of the learning unit is updated.
 4. The controlmethod according to claim 1, wherein by images used for the secondlayout data being arranged in a template based on the generated albumcreation parameter, the layout data is generated.
 5. The control methodaccording to claim 1, wherein the first layout data and the secondlayout data are layout data acquired from a plurality of pieces oflayout data uploaded to a server after being generated by an albumcreation application in a terminal device of a user.
 6. The controlmethod according to claim 5, wherein a number of items of the albumcreation parameter to be updated in a case where a number of pieces oflayout data uploaded to the server is large is larger than a number ofitems of the album creation parameter to be updated in a case where anumber of pieces of layout data uploaded to the server is small.
 7. Thecontrol method according to claim 1, wherein in a case where the updatedalbum creation parameter satisfies a predetermined condition, theupdated album creation parameter is downloaded to a terminal device of auser by updating of an album creation application.
 8. The control methodaccording to claim 7, wherein the downloaded updated album creationparameter is used at the time of generating new layout data by the albumcreation application.
 9. The control method according to claim 1,wherein verification is performed by using layout data different fromthe first layout data and the second layout data.